Q: Discuss the differences between linear and nonlinear optimization methods. Include specific examples of nonlinear optimization methods and discuss which aspects of these models make them different from traditional linear optimization methods.

Student: A nonlinear optimization problem is one in which the objective or at least one of the constraints is a smooth nonlinear function of the decision variables. Nonlinear functions, unlike linear functions, may involve variables that are raised to a power or multiplied or divided by other variables and may also use transcendental functions such as exp, log, sine and cosine. Linear optimizaton models follow a linear trend and the objective is linear in the unknowns, and the constraints are linear equalities or linear inequalities in the unknowns.

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